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<a href="_lasso_regression_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       LASSO Regression</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> *</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> *</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * \author      T. Glasmachers</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \date        2013</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> *</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> *</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> *</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> *</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> *</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> *</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> */</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span> </div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_TRAINERS_LASSOREGRESSION_H</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#define SHARK_ALGORITHMS_TRAINERS_LASSOREGRESSION_H</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_linear_model_8h.html">shark/Models/LinearModel.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_trainer_8h.html" title="Abstract Trainer Interface.">shark/Algorithms/Trainers/AbstractTrainer.h</a>&gt;</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="preprocessor">#include &lt;cmath&gt;</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span> </div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span> </div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span> </div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span> </div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment"></span> </div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">///  \brief LASSO Regression</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">///</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">///  LASSO Regression extracts a sparse vector of regression</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">///  coefficients. The original method amounts to L1-constrained</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">///  least squares regression, while this implementation uses an</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">///  L1 penalty instead of a constraint (which is equivalent).</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">///</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">///  For data vectors \f$ x_i \f$ with real-valued labels \f$ y_i \f$</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">///  the trainer solves the problem</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">///  \f$ \min_w \quad \frac{1}{2} \sum_i (w^T x_i - y_i)^2 + \lambda \|w\|_1 \f$.</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">///  The target accuracy of the solution is measured in terms of the</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">///  smallest component of the gradient of the objective function.</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">///</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">///  The trainer has one template parameter, namely the type of</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">///  the input vectors \f$ x_i \f$. These need to be vector valued,</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment">///  typically either RealVector of CompressedRealVector. The</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">///  resulting weight vector w is represented by a LinearModel</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment">///  object. Currently model outputs and labels are restricted to a</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment">///  single dimension.</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="comment">/// \ingroup supervised_trainer</span></div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="comment"></span><span class="keyword">template</span> &lt;<span class="keyword">class</span> InputVectorType = RealVector&gt;</div>
<div class="foldopen" id="foldopen00069" data-start="{" data-end="};">
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"><a class="line" href="classshark_1_1_lasso_regression.html">   69</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_lasso_regression.html" title="LASSO Regression.">LassoRegression</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_trainer.html" title="Superclass of supervised learning algorithms.">AbstractTrainer</a>&lt;LinearModel&lt;InputVectorType&gt; &gt;, <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_i_parameterizable.html" title="Top level interface for everything that holds parameters.">IParameterizable</a>&lt;&gt;</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>{</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"><a class="line" href="classshark_1_1_lasso_regression.html#aaa5953a1525e6777f08097307bf5b792">   72</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_linear_model.html" title="Linear Prediction with optional activation function.">LinearModel&lt;InputVectorType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_lasso_regression.html#aaa5953a1525e6777f08097307bf5b792">ModelType</a>;</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"><a class="line" href="classshark_1_1_lasso_regression.html#ad09457449981c6e602f53d65d184c1a7">   73</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputVectorType, RealVector&gt;</a> <a class="code hl_typedef" href="classshark_1_1_lasso_regression.html#ad09457449981c6e602f53d65d184c1a7">DataType</a>;</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span><span class="comment"></span> </div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span><span class="comment">    /// \brief Constructor.</span></div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span><span class="comment">    /// \param  lambda    value of the regularization parameter (see class description)</span></div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment">    /// \param  accuracy  stopping criterion for the iterative solver, maximal gradient component of the objective function (see class description)</span></div>
<div class="foldopen" id="foldopen00079" data-start="{" data-end="}">
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"><a class="line" href="classshark_1_1_lasso_regression.html#a6d72b15b0f10134187f75ecf51d2dde2">   79</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a6d72b15b0f10134187f75ecf51d2dde2" title="Constructor.">LassoRegression</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8" title="Return the current setting of the regularization parameter.">lambda</a>, <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a9b0659679b615f23713ac8a3669e6de1" title="Return the current setting of the accuracy (maximal gradient component of the optimization problem).">accuracy</a> = 0.01)</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>    : <a class="code hl_variable" href="classshark_1_1_lasso_regression.html#a5cb2b80c46682ceaf024501043bd6cb8" title="regularization parameter">m_lambda</a>(<a class="code hl_function" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8" title="Return the current setting of the regularization parameter.">lambda</a>)</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>    , <a class="code hl_variable" href="classshark_1_1_lasso_regression.html#af10df12bdcf1d2b03a71c5867a51cd9f" title="gradient accuracy">m_accuracy</a>(<a class="code hl_function" href="classshark_1_1_lasso_regression.html#a9b0659679b615f23713ac8a3669e6de1" title="Return the current setting of the accuracy (maximal gradient component of the optimization problem).">accuracy</a>)</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>    {</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(<a class="code hl_variable" href="classshark_1_1_lasso_regression.html#a5cb2b80c46682ceaf024501043bd6cb8" title="regularization parameter">m_lambda</a> &gt;= 0.0);</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(<a class="code hl_variable" href="classshark_1_1_lasso_regression.html#af10df12bdcf1d2b03a71c5867a51cd9f" title="gradient accuracy">m_accuracy</a> &gt; 0.0);</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>    }</div>
</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span><span class="comment"></span> </div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00088" data-start="{" data-end="}">
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"><a class="line" href="classshark_1_1_lasso_regression.html#ad8508272dd9a2c5821c85587c26dd3b2">   88</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_lasso_regression.html#ad8508272dd9a2c5821c85587c26dd3b2" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;LASSO regression&quot;</span>; }</div>
</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span> </div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span><span class="comment"></span> </div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span><span class="comment">    /// \brief Return the current setting of the regularization parameter.</span></div>
<div class="foldopen" id="foldopen00093" data-start="{" data-end="}">
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"><a class="line" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8">   93</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8" title="Return the current setting of the regularization parameter.">lambda</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_lasso_regression.html#a5cb2b80c46682ceaf024501043bd6cb8" title="regularization parameter">m_lambda</a>;</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>    }</div>
</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span><span class="comment"></span> </div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span><span class="comment">    /// \brief Set the regularization parameter.</span></div>
<div class="foldopen" id="foldopen00099" data-start="{" data-end="}">
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"><a class="line" href="classshark_1_1_lasso_regression.html#ad0a5195a4cf6ad535238f9330e61abd9">   99</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_lasso_regression.html#ad0a5195a4cf6ad535238f9330e61abd9" title="Set the regularization parameter.">setLambda</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8" title="Return the current setting of the regularization parameter.">lambda</a>)</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>    {</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(<a class="code hl_function" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8" title="Return the current setting of the regularization parameter.">lambda</a> &gt;= 0.0);</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>        <a class="code hl_variable" href="classshark_1_1_lasso_regression.html#a5cb2b80c46682ceaf024501043bd6cb8" title="regularization parameter">m_lambda</a> = <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8" title="Return the current setting of the regularization parameter.">lambda</a>;</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    }</div>
</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span><span class="comment"></span> </div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span><span class="comment">    /// \brief Return the current setting of the accuracy (maximal gradient component of the optimization problem).</span></div>
<div class="foldopen" id="foldopen00106" data-start="{" data-end="}">
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"><a class="line" href="classshark_1_1_lasso_regression.html#a9b0659679b615f23713ac8a3669e6de1">  106</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a9b0659679b615f23713ac8a3669e6de1" title="Return the current setting of the accuracy (maximal gradient component of the optimization problem).">accuracy</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_lasso_regression.html#af10df12bdcf1d2b03a71c5867a51cd9f" title="gradient accuracy">m_accuracy</a>;</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>    }</div>
</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span><span class="comment"></span> </div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span><span class="comment">    /// \brief Set the accuracy (maximal gradient component of the optimization problem).</span></div>
<div class="foldopen" id="foldopen00112" data-start="{" data-end="}">
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"><a class="line" href="classshark_1_1_lasso_regression.html#a02b6de49b279a6bb90e1fcbd213a889f">  112</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a02b6de49b279a6bb90e1fcbd213a889f" title="Set the accuracy (maximal gradient component of the optimization problem).">setAccuracy</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a9b0659679b615f23713ac8a3669e6de1" title="Return the current setting of the accuracy (maximal gradient component of the optimization problem).">accuracy</a>)</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>    {</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(<a class="code hl_function" href="classshark_1_1_lasso_regression.html#a9b0659679b615f23713ac8a3669e6de1" title="Return the current setting of the accuracy (maximal gradient component of the optimization problem).">accuracy</a> &gt; 0.0);</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>        <a class="code hl_variable" href="classshark_1_1_lasso_regression.html#af10df12bdcf1d2b03a71c5867a51cd9f" title="gradient accuracy">m_accuracy</a> = <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a9b0659679b615f23713ac8a3669e6de1" title="Return the current setting of the accuracy (maximal gradient component of the optimization problem).">accuracy</a>;</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>    }</div>
</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span><span class="comment"></span> </div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span><span class="comment">    /// \brief Get the regularization parameter lambda through the IParameterizable interface.</span></div>
<div class="foldopen" id="foldopen00119" data-start="{" data-end="}">
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"><a class="line" href="classshark_1_1_lasso_regression.html#aa23ec16704158ce8b7f5d7f501f4429a">  119</a></span><span class="comment"></span>    RealVector <a class="code hl_function" href="classshark_1_1_lasso_regression.html#aa23ec16704158ce8b7f5d7f501f4429a" title="Get the regularization parameter lambda through the IParameterizable interface.">parameterVector</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>        <span class="keywordflow">return</span> RealVector(1, <a class="code hl_variable" href="classshark_1_1_lasso_regression.html#a5cb2b80c46682ceaf024501043bd6cb8" title="regularization parameter">m_lambda</a>);</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>    }</div>
</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span><span class="comment"></span> </div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span><span class="comment">    /// \brief Set the regularization parameter lambda through the IParameterizable interface.</span></div>
<div class="foldopen" id="foldopen00125" data-start="{" data-end="}">
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"><a class="line" href="classshark_1_1_lasso_regression.html#ac51f1a84959f0084f23490a7dcf2b7cb">  125</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_lasso_regression.html#ac51f1a84959f0084f23490a7dcf2b7cb" title="Set the regularization parameter lambda through the IParameterizable interface.">setParameterVector</a>(<span class="keyword">const</span> RealVector&amp; param)</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>    {</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(param.size() == 1);</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(param(0) &gt;= 0.0);</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>        <a class="code hl_variable" href="classshark_1_1_lasso_regression.html#a5cb2b80c46682ceaf024501043bd6cb8" title="regularization parameter">m_lambda</a> = param(0);</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>    }</div>
</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span><span class="comment"></span> </div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span><span class="comment">    /// \brief Return the number of parameters (one in this case).</span></div>
<div class="foldopen" id="foldopen00133" data-start="{" data-end="}">
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"><a class="line" href="classshark_1_1_lasso_regression.html#a5ed477193272500aedbb7f1191fb8e29">  133</a></span><span class="comment"></span>    <span class="keywordtype">size_t</span> <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a5ed477193272500aedbb7f1191fb8e29" title="Return the number of parameters (one in this case).">numberOfParameters</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        <span class="keywordflow">return</span> 1;</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>    }</div>
</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span><span class="comment"></span> </div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span><span class="comment">    /// \brief Train a linear model with LASSO regression.</span></div>
<div class="foldopen" id="foldopen00139" data-start="{" data-end="}">
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno"><a class="line" href="classshark_1_1_lasso_regression.html#acf451db20a82eef8547629c28db9db4e">  139</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_lasso_regression.html#acf451db20a82eef8547629c28db9db4e" title="Train a linear model with LASSO regression.">train</a>(<a class="code hl_class" href="classshark_1_1_linear_model.html" title="Linear Prediction with optional activation function.">ModelType</a>&amp; model, <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">DataType</a> <span class="keyword">const</span>&amp; dataset){</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span> </div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>        <span class="comment">// strategy constants</span></div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>        <span class="keyword">const</span> <span class="keywordtype">double</span> CHANGE_RATE = 0.2;</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        <span class="keyword">const</span> <span class="keywordtype">double</span> PREF_MIN = 0.05;</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>        <span class="keyword">const</span> <span class="keywordtype">double</span> PREF_MAX = 20.0;</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span> </div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>        <span class="comment">// console output</span></div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>        <span class="keyword">const</span> <span class="keywordtype">bool</span> verbose = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span> </div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>        std::size_t dim = <a class="code hl_function" href="group__shark__globals.html#gae537f0e90beb970397cd7bb9250984e2" title="Return the input dimensionality of a labeled dataset.">inputDimension</a>(dataset);</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>        RealVector w(dim, 0.0);</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span> </div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        <span class="comment">// transpose the dataset and push it inside a single matrix</span></div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        <span class="keyword">auto</span> data = <a class="code hl_function" href="namespaceshark.html#a5478d144c4c997faf5c246dd8e2f85b8" title="creates a batch from a range of inputs">createBatch</a>(dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>().<a class="code hl_function" href="group__shark__globals.html#gad9b0233e3adc882ed94f418f80767b09" title="Returns the range of elements.">elements</a>());</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        data = trans(data);</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>        <span class="keyword">auto</span> label_mat = <a class="code hl_function" href="namespaceshark.html#a5478d144c4c997faf5c246dd8e2f85b8" title="creates a batch from a range of inputs">createBatch</a>(dataset.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>().<a class="code hl_function" href="group__shark__globals.html#gad9b0233e3adc882ed94f418f80767b09" title="Returns the range of elements.">elements</a>());</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>        RealVector label = column(label_mat,0);</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span> </div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>        RealVector diag(dim);</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        RealVector difference = -label;</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>        UIntVector index(dim);</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span> </div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>        <span class="comment">// pre-calculate diagonal matrix entries (feature-wise squared norms)</span></div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i=0; i&lt;dim; i++){</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>            diag[i] = norm_sqr(row(data,i));</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>        }</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span> </div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        <span class="comment">// prepare preferences for scheduling</span></div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>        RealVector pref(dim, 1.0);</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        <span class="keywordtype">double</span> prefsum = (double)dim;</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span> </div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>        <span class="comment">// prepare performance monitoring for self-adaptation</span></div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>        <span class="keyword">const</span> <span class="keywordtype">double</span> gain_learning_rate = 1.0 / dim;</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        <span class="keywordtype">double</span> average_gain = 0.0;</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        <span class="keywordtype">bool</span> canstop = <span class="keyword">true</span>;</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>        <span class="keyword">const</span> <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8" title="Return the current setting of the regularization parameter.">lambda</a> = <a class="code hl_variable" href="classshark_1_1_lasso_regression.html#a5cb2b80c46682ceaf024501043bd6cb8" title="regularization parameter">m_lambda</a>;</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span> </div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>        <span class="comment">// main optimization loop</span></div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>        std::size_t iter = 0;</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>        std::size_t steps = 0;</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>        <span class="keywordflow">while</span> (<span class="keyword">true</span>)</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>        {</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>            <span class="keywordtype">double</span> maxvio = 0.0;</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span> </div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>            <span class="comment">// define schedule</span></div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>            <span class="keywordtype">double</span> psum = prefsum;</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>            prefsum = 0.0;</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>            <span class="keywordtype">int</span> pos = 0;</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span> </div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>            <span class="keywordflow">for</span> (std::size_t i=0; i&lt;dim; i++)</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>            {</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>                <span class="keywordtype">double</span> p = pref[i];</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>                <span class="keywordtype">double</span> n;</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>                <span class="keywordflow">if</span> (psum &gt;= 1e-6 &amp;&amp; p &lt; psum)</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>                    n = (dim - pos) * p / psum;</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>                <span class="keywordflow">else</span></div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>                    n = (double)(dim - pos);          <span class="comment">// for numerical stability</span></div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span> </div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>                <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> m = (<span class="keywordtype">unsigned</span> int)floor(n);</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>                <span class="keywordtype">double</span> prob = n - m;</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>                <span class="keywordflow">if</span> ((<span class="keywordtype">double</span>)rand() / (double)RAND_MAX &lt; prob) m++;</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>                <span class="keywordflow">for</span> (std::size_t  j=0; j&lt;m; j++)</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>                {</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>                    index[pos] = (<span class="keywordtype">unsigned</span> int)i;</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>                    pos++;</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>                }</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>                psum -= p;</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>                prefsum += p;</div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>            }</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>            <span class="keywordflow">for</span> (std::size_t i=0; i&lt;dim; i++)</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>            {</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>                std::size_t r = rand() % dim;</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>                std::swap(index[r], index[i]);</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>            }</div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span> </div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>            steps += dim;</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> s=0; s&lt;dim; s++)</div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>            {</div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>                std::size_t i = index[s];</div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>                <span class="keywordtype">double</span> a = w[i];</div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span>                <span class="keywordtype">double</span> d = diag[i];</div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span> </div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span>                <span class="comment">// compute gradient</span></div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>                <span class="keywordtype">double</span> grad = inner_prod(difference, row(data,i));</div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span> </div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span>                <span class="comment">// compute optimal coordinate descent step and corresponding gain</span></div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span>                <span class="keywordtype">double</span> vio = 0.0;</div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span>                <span class="keywordtype">double</span> gain = 0.0;</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>                <span class="keywordtype">double</span> delta = 0.0;</div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>                <span class="keywordflow">if</span> (a == 0.0)</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>                {</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>                    <span class="keywordflow">if</span> (grad &gt; <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8" title="Return the current setting of the regularization parameter.">lambda</a>)</div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>                    {</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span>                        vio = grad - <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8" title="Return the current setting of the regularization parameter.">lambda</a>;</div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span>                        delta = -vio / d;</div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>                        gain = 0.5 * d * delta * delta;</div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span>                    }</div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span>                    <span class="keywordflow">else</span> <span class="keywordflow">if</span> (grad &lt; -<a class="code hl_function" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8" title="Return the current setting of the regularization parameter.">lambda</a>)</div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>                    {</div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>                        vio = -grad - <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8" title="Return the current setting of the regularization parameter.">lambda</a>;</div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>                        delta = vio / d;</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>                        gain = 0.5 * d * delta * delta;</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>                    }</div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>                }</div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span>                <span class="keywordflow">else</span> <span class="keywordflow">if</span> (a &gt; 0.0)</div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>                {</div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>                    grad += <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8" title="Return the current setting of the regularization parameter.">lambda</a>;</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>                    vio = std::fabs(grad);</div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>                    delta = -grad / d;</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>                    <span class="keywordflow">if</span> (delta &lt; -a)</div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>                    {</div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span>                        delta = -a;</div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>                        gain = delta * (grad - 0.5 * d * delta);</div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>                        <span class="keywordtype">double</span> g0 = grad - a * d - 2.0 * <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8" title="Return the current setting of the regularization parameter.">lambda</a>;</div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span>                        <span class="keywordflow">if</span> (g0 &gt; 0.0)</div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>                        {</div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>                            <span class="keywordtype">double</span> dd = -g0 / d;</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>                            gain = dd * (grad - 0.5 * d * dd);</div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span>                            delta += dd;</div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span>                        }</div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>                    }</div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span>                    <span class="keywordflow">else</span> gain = 0.5 * d * delta * delta;</div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>                }</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>                <span class="keywordflow">else</span></div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>                {</div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>                    grad -= <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8" title="Return the current setting of the regularization parameter.">lambda</a>;</div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>                    vio = std::fabs(grad);</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span>                    delta = -grad / d;</div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span>                    <span class="keywordflow">if</span> (delta &gt; -a)</div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>                    {</div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>                        delta = -a;</div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span>                        gain = delta * (grad - 0.5 * d * delta);</div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>                        <span class="keywordtype">double</span> g0 = grad - a * d + 2.0 * <a class="code hl_function" href="classshark_1_1_lasso_regression.html#a424828aaade483a29aab0382ebbaceb8" title="Return the current setting of the regularization parameter.">lambda</a>;</div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span>                        <span class="keywordflow">if</span> (g0 &lt; 0.0)</div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span>                        {</div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span>                            <span class="keywordtype">double</span> dd = -g0 / d;</div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span>                            gain = dd * (grad - 0.5 * d * dd);</div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span>                            delta += dd;</div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno">  278</span>                        }</div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span>                    }</div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span>                    <span class="keywordflow">else</span> gain = 0.5 * d * delta * delta;</div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span>                }</div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span> </div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno">  283</span>                <span class="comment">// update state</span></div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span>                <span class="keywordflow">if</span> (vio &gt; maxvio) maxvio = vio;</div>
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno">  285</span>                <span class="keywordflow">if</span> (delta != 0.0)</div>
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno">  286</span>                {</div>
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno">  287</span>                    w[i] += delta;</div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno">  288</span>                    noalias(difference) += delta*row(data,i);</div>
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno">  289</span>                }</div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno">  290</span> </div>
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno">  291</span>                <span class="comment">// update gain-based preferences</span></div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno">  292</span>                {</div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno">  293</span>                    <span class="keywordflow">if</span> (iter == 0)</div>
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno">  294</span>                        average_gain += gain / (double)dim;</div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno">  295</span>                    <span class="keywordflow">else</span></div>
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno">  296</span>                    {</div>
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno">  297</span>                        <span class="keywordtype">double</span> change = CHANGE_RATE * (gain / average_gain - 1.0);</div>
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno">  298</span>                        <span class="keywordtype">double</span> newpref = pref[i] * std::exp(change);</div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno">  299</span>                        newpref = std::min(std::max(newpref, PREF_MIN), PREF_MAX);</div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno">  300</span>                        prefsum += newpref - pref[i];</div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno">  301</span>                        pref[i] = newpref;</div>
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno">  302</span>                        average_gain = (1.0 - gain_learning_rate) * average_gain + gain_learning_rate * gain;</div>
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno">  303</span>                    }</div>
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno">  304</span>                }</div>
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno">  305</span>            }</div>
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno">  306</span>            iter++;</div>
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno">  307</span> </div>
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno">  308</span>            <span class="keywordflow">if</span> (maxvio &lt;= <a class="code hl_variable" href="classshark_1_1_lasso_regression.html#af10df12bdcf1d2b03a71c5867a51cd9f" title="gradient accuracy">m_accuracy</a>)</div>
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno">  309</span>            {</div>
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno">  310</span>                <span class="keywordflow">if</span> (canstop)</div>
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno">  311</span>                    <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno">  312</span>                <span class="keywordflow">else</span></div>
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno">  313</span>                {</div>
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno">  314</span>                    <span class="comment">// prepare full sweep for a reliable check of the stopping criterion</span></div>
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno">  315</span>                    canstop = <span class="keyword">true</span>;</div>
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno">  316</span>                    noalias(pref) = blas::repeat(1.0, dim);</div>
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno">  317</span>                    prefsum = (double)dim;</div>
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno">  318</span>                    <span class="keywordflow">if</span> (verbose) std::cout &lt;&lt; <span class="stringliteral">&quot;*&quot;</span> &lt;&lt; std::flush;</div>
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno">  319</span>                }</div>
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno">  320</span>            }</div>
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno">  321</span>            <span class="keywordflow">else</span></div>
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno">  322</span>            {</div>
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno">  323</span>                canstop = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno">  324</span>                <span class="keywordflow">if</span> (verbose) std::cout &lt;&lt; <span class="stringliteral">&quot;.&quot;</span> &lt;&lt; std::flush;</div>
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno">  325</span>            }</div>
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno">  326</span>        }</div>
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno">  327</span> </div>
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno">  328</span>        <span class="comment">// write the weight vector into the model</span></div>
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno">  329</span>        RealMatrix mat(1, w.size());</div>
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno">  330</span>        row(mat, 0) = w;</div>
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno">  331</span>        model.<a class="code hl_function" href="classshark_1_1_linear_model.html#a901efd377ffaf2d09a50d2adcbd6f9d4" title="overwrite structure and parameters">setStructure</a>(mat);</div>
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno">  332</span>    }</div>
</div>
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno">  333</span> </div>
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno">  334</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno"><a class="line" href="classshark_1_1_lasso_regression.html#a5cb2b80c46682ceaf024501043bd6cb8">  335</a></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_lasso_regression.html#a5cb2b80c46682ceaf024501043bd6cb8" title="regularization parameter">m_lambda</a>;             <span class="comment">///&lt; regularization parameter</span></div>
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno"><a class="line" href="classshark_1_1_lasso_regression.html#af10df12bdcf1d2b03a71c5867a51cd9f">  336</a></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_lasso_regression.html#af10df12bdcf1d2b03a71c5867a51cd9f" title="gradient accuracy">m_accuracy</a>;           <span class="comment">///&lt; gradient accuracy</span></div>
<div class="line"><a id="l00337" name="l00337"></a><span class="lineno">  337</span>};</div>
</div>
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno">  338</span> </div>
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno">  339</span> </div>
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno">  340</span>}</div>
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno">  341</span><span class="preprocessor">#endif</span></div>
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